在Python 2.7即将停止支持时,我们为你带来了一份python 3.x迁移指南

# Python 3
result = process(x) if is_valid(x) else print('invalid item: ', x)

# Python 2
print('{batch:3} {epoch:3} / {total_epochs:3} accuracy: {acc_mean:0.4f}±{acc_std:0.4f} time: {avg_time:3.2f}'.format(
 batch=batch, epoch=epoch, total_epochs=total_epochs,
 acc_mean=numpy.mean(accuracies), acc_std=numpy.std(accuracies),
 avg_time=time / len(data_batch)
))
# Python 2 (too error-prone during fast modifications, please avoid):
print('{:3} {:3} / {:3} accuracy: {:0.4f}±{:0.4f} time: {:3.2f}'.format(
 batch, epoch, total_epochs, numpy.mean(accuracies), numpy.std(accuracies),
 time / len(data_batch)
))

120 12 / 300 accuracy: 0.8180±0.4649 time: 56.60

# Python 3.6+
print(f'{batch:3} {epoch:3} / {total_epochs:3} accuracy: {numpy.mean(accuracies):0.4f}±{numpy.std(accuracies):0.4f} time: {time / len(data_batch):3.2f}')

query = f"INSERT INTO STATION VALUES (13, '{city}', '{state}', {latitude}, {longitude})"

data = pandas.read_csv('timing.csv')
velocity = data['distance'] / data['time']

n_gifts = money // gift_price # correct for int and float arguments

# All these comparisons are illegal in Python 3
3 < '3'
2 < None
(3, 4) < (3, None)
(4, 5) < [4, 5]
# False in both Python 2 and Python 3
(4, 5) == [4, 5]
防止不同类型实例的偶然性的排序。
sorted([2, '1', 3]) # invalid for Python 3, in Python 2 returns [2, 3, '1']
在处理原始数据时帮助发现存在的问题。
旁注：对 None 的合适检查是（两个版本的 Python 都适用）：
if a is not None:
 pass
if a: # WRONG check for None
 pass

s = '您好'
print(len(s))
print(s[:2])

Python 2: 6\n��
Python 3: 2\n 您好.
x = u'со'
x += 'co' # ok
x += 'со' # fail

'a' < type < u'a' # Python 2: True
'a' < u'a'   # Python 2: False
from collections import Counter
Counter('Möbelstück')
Python 2: Counter({'\xc3': 2, 'b': 1, 'e': 1, 'c': 1, 'k': 1, 'M': 1, 'l': 1, 's': 1, 't': 1, '\xb6': 1, '\xbc': 1})
Python 3: Counter({'M': 1, 'ö': 1, 'b': 1, 'e': 1, 'l': 1, 's': 1, 't': 1, 'ü': 1, 'c': 1, 'k': 1})

import json
x = {str(i):i for i in range(5)}
json.loads(json.dumps(x))
# Python 2
{u'1': 1, u'0': 0, u'3': 3, u'2': 2, u'4': 4}
# Python 3
{'0': 0, '1': 1, '2': 2, '3': 3, '4': 4}

from torch import nn
# Python 2
model = nn.Sequential(OrderedDict([
   ('conv1', nn.Conv2d(1,20,5)),
   ('relu1', nn.ReLU()),
   ('conv2', nn.Conv2d(20,64,5)),
   ('relu2', nn.ReLU())
  ]))
# Python 3.6+, how it *can* be done, not supported right now in pytorch
model = nn.Sequential(
 conv1=nn.Conv2d(1,20,5),
 relu1=nn.ReLU(),
 conv2=nn.Conv2d(20,64,5),
 relu2=nn.ReLU())
) 

# handy when amount of additional stored info may vary between experiments, but the same code can be used in all cases
model_paramteres, optimizer_parameters, *other_params = load(checkpoint_name)
# picking two last values from a sequence
*prev, next_to_last, last = values_history
# This also works with any iterables, so if you have a function that yields e.g. qualities,
# below is a simple way to take only last two values from a list
*prev, next_to_last, last = iter_train(args)

# Python 2
import cPickle as pickle
import numpy
print len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))
# result: 23691675
# Python 3
import pickle
import numpy
len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))
# result: 8000162

labels = 
predictions = [model.predict(data) for data, labels in dataset]
# labels are overwritten in Python 2
# labels are not affected by comprehension in Python 3

# Python 2
class MySubClass(MySuperClass):
 def __init__(self, name, **options):
  super(MySubClass, self).__init__(name='subclass', **options)
# Python 3
class MySubClass(MySuperClass):
 def __init__(self, name, **options):
  super().__init__(name='subclass', **options)

x = dict(a=1, b=2)
y = dict(b=3, d=4)
# Python 3.5+
z = {**x, **y}
# z = {'a': 1, 'b': 3, 'd': 4}, note that value for `b` is taken from the latter dict.

[*a, *b, *c] # list, concatenating
(*a, *b, *c) # tuple, concatenating
{*a, *b, *c} # set, union

Python 3.5+
do_something(**{**default_settings, **custom_settings})
# Also possible, this code also checks there is no intersection between keys of dictionaries
do_something(**first_args, **second_args)

model = sklearn.svm.SVC(2, 'poly', 2, 4, 0.5)

class SVC(BaseSVC):
 def __init__(self, *, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, ... )

# Python 3
math.inf # 'largest' number
math.nan # not a number
max_quality = -math.inf # no more magic initial values!
for model in trained_models:
 max_quality = max(max_quality, compute_quality(model, data)

isinstance(x, numbers.Integral) # Python 2, the canonical way
isinstance(x, (long, int))  # Python 2
isinstance(x, int)    # Python 3, easier to remember